code: def test_inference_no_head(self):
      model = MPNetModel.from_pretrained("microsoft/mpnet-base")
      input_ids = torch.tensor(
          [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]
      )
      output = model(input_ids)[0]
      expected_shape = torch.Size((1, 11, 768))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [-0.0550, 0.1943, -0.0740],
                  [-0.0562, 0.2211, -0.0579],
                  [-0.0437, 0.3337, -0.0641],
              ]
          ]
      )
      # compare the actual values for a slice.
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import MPNetConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the MPNetModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head() without return types